Brands including Ralph Lauren, Sperry, Lucky Brand, MM LaFleur and True Religion are turning to a predictive analytics platform called Makersights to help inform their product design and development.

Think of a traditional focus group where learnings are high, but costly and slow, and then tip it into the digital, mobile or indeed machine learning age and you’re on the right page. This is a business that pulls information at scale from customer insights, then applies actual sales data and machine learning to that feedback. They call it "actionable product intelligence".

According to the team, the aim is to help brand partners develop more accurate sales plans, de-risk new product introductions and measure how customers respond to product attributes like fabrics, colors and price. It’s already seeing a 2-4% gross margin lift for its partners based on minimizing markdowns and doubling down on big winners.

As Bryan Fogg, VP of global customer intelligence and experience management at Ralph Lauren, says in a press release: "MakerSights allows us to better understand how design details and product attributes resonate with our customers to help inform our product decision making. Our goal is to create products our customers love, and MakerSights helps us to do that with more confidence by engaging our customers directly."

The interesting thing is what all that says for the role of data and creativity in today’s design businesses, and just how much the future really can be shaped by data science.

I sat down with Matt Field, co-founder and president of MakerSights, to find out more...

RA: What’s the need behind a service like yours?

MF: Every retail brand or partner in the space is grappling with the same macro question: “How can we be successful in a world in which supply and demand dynamics have shifted so dynamically?” Consumption has changed so much in the past five years. There’s increased competition, from Amazon and Walmart to up and coming direct-to-consumer brands. Plus customers are more discerning than they were – they no longer want trends dictated to them.

We’re helping brands successfully develop product in this new environment. Speed and accuracy is of essence. The goal is to turn savvy customer bases into an asset – we capture structured insights from end customers on future product and then apply predictive analytics to enable brand partners to more accurately forecast demand for expected sales.

RA: In a more detailed sense, what does that mean the Makersights platform actually does?

MF: There are two primary components to the platform; the first is about data capture. To get accurate and meaningful insights for brands, we think there is a better way to engage customers… the way we do that is built to be really fast, engaging and on-brand. If you have a problem with a flight and they send you a survey, it’s a really clunky experience – as a consumer you have no incentive to provide feedback. Or what you’re getting is from incredibly high brand loyalists who will respond to anything; so it’s not representative of your true broader customer base.

So our goal is to be much more representative. How it actually works is that the brands will engage with customers through email and social media, they will direct to our platform which is built in the look, feel and tone of voice of the brand partner, then give short piece of structured feedback about what they like or don’t like about a product. It takes 60-90 seconds.

At the end of the feedback experiences, we capture how many would be willing to participate again. The average is a 98% positive response rate, which shows how interested consumers are in co-creation with a brand.

The second component post data capture, is then about the machine learning and predictive analytics we do. At this point, we’ve tested thousands of products with hundreds of thousands of customers. We capture what they think about a product, what sort of customer they are, to what evaluations they’ve given in the past… then pair that with sales data and performance. When we put that into the model, it gets smarter every time we test more products, enabling us to be increasingly accurate when forecasting demand. What we’re trying to identify is if a product is going to outsell, be inline with or underwhelm expectations. We’re able to predict 6, 9, 12 months out how well it will sell once it hits stores or e-commerce sites.

MM LaFleur's Makersights dashboard

Makersights

RA: Why your platform, what’s the incentive for the consumer?

MF: Providing a monetary incentive we found lowered overall value… so we strongly encourage [against it]. What we find as drivers of excitement, are around the way we open up the kimono, so to speak, and allow consumers to participate in the design process they’ve not previously had access to.

The other thing that separates us is the branded experience and the way we keep it really short. We find 75% of people taking our surveys are doing so on mobile… that will only grow, it will be 80-85% by the end of this year. It looks and feels like an e-commerce experience and taps out at 60 seconds, so you could do it on the train or going to a meeting… it’s almost like a fun way to give a brand a quick piece of advice on how they weigh up compared to expectations.

We really believe customers want to interact with brands in a new way than they did before. As they participate and feedback, there’s not only high engagement, but the marketing impact of the data we’re collecting we also think is exciting. When we do a sample test with Ralph Lauren for instance, they’ll often collect feedback from 5,000-10,000 customers. That’s a big group with very clear signals of purchase intent, so they can use that for follow up marketing, targeting… they’ve told you exactly what they like.

We feedback when products launch and use that to create personalization. We’ve seen up to 8x lift as a result of targeting customers and saying to them “you spoke, we listened and produced the exact product you wanted”. We get feedback from customers saying how empowering that is. They feel like a brand they have an emotional connection to has taken the time to listen to them.

RA: Do you see yourselves as a modern day focus group?

MF: What we find brand partners are struggling with is how to get info at scale really rapidly to make informed decisions… When they invest in a new category the bets they’re making are massive, in tens of millions sometimes. And those bets are being made on very short timelines yet sometimes on sales data that’s often two years old. The tools they have had, like focus groups, are so limited – 50 people is just not representative enough.

What we see ourselves doing is replacing focus groups, or to think about it in a new world of retail, offering how you gain more immediacy and accuracy in terms of your collections. With the machine learning and predictive analytics, the results are also more grounded and actionable.

We did a lot of testing on who the most accurate sources of information are for a brand, and your own customers are the most important place you can be listening.

RA: What do you think the role of data is in fashion creative today?

MF: I think that’s a fascinating question and one we spend the majority of our time thinking about. We don’t think our platform should be used to crowdsource an assortment. We believe in [the brand’s] identity, in them taking a stand on what they’re creating and who for, and we don’t want to undermine that in any way.

What we believe in is that a robust systematic product intelligence platform can greatly empower creativity. We can provide focus, faster decision-making and confidence. Our team is half data scientists and the other half from industry – former planners and merchants. That balance of creativity and science is a huge part of how we built our company.